{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# INST728E - Module 5. Temporal Analysis\n",
    "\n",
    "In this section, we will cover a few simple analysis techniques to garner some small insights rapidly.\n",
    "\n",
    "- Time parsing\n",
    "- Frequency Graph\n",
    "- Keyword Frequency\n",
    "\n",
    "### Zooming in on an event\n",
    "\n",
    "Then we'll look at relevant tweets and answer some questions:\n",
    "\n",
    "- Top users\n",
    "- Top hash tags\n",
    "- Top URLs\n",
    "- Top images\n",
    "- Most retweeted tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import datetime\n",
    "import json\n",
    "import gzip\n",
    "import glob\n",
    "import os\n",
    "import string\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import nltk\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining An Event\n",
    "\n",
    "The following notebook walks us through a number of capabilities or common pieces of functionality one may want when analyzing Twitter following a crisis.\n",
    "We will start by defining information for an event for which we have data.\n",
    "\n",
    "You can replace this dictionary or add to it with an event of your own! In fact, you'll need to do so for this module's homework."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "crisisInfo = {\n",
    "    \"Women's March\": {\n",
    "        \"name\": \"Women's March 2017\",\n",
    "        \"time\": 1484992800, # 21 January 2017, 6:58 UTC to 08:11 UTC\n",
    "        \"directory\": \"womensmarch\",    # Where do we find the relevant files\n",
    "        \"keywords\": [    # How can we describe this event?\n",
    "            \"women's march\",\"resist\", \"notmypresident\",\"inauguration\",\"women's right\",\"human right\",\"planned parenthood\"\n",
    "        ],\n",
    "        \"box\": {    # Where did this event occur?\n",
    "            \"lowerLeftLon\": 2.54563,\n",
    "            \"lowerLeftLat\": 49.496899,\n",
    "            \"upperRightLon\": 6.40791,\n",
    "            \"upperRightLat\": 51.5050810,\n",
    "        }\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "## Reading Tweets\n",
    "\n",
    "The first thing we do is read in tweets from a directory of compressed files. Our collection of compressed tweets is in the 00_data directory, so we'll use pattern matching (called \"globbing\") to find all the tweet files in the given directory.\n",
    "\n",
    "Then, for each file, we'll open it, read each line (which is a tweet in JSON form), and build an object out of it. As part of this process, we will extract each tweet's post time and create a map from minute timestamps to the tweets posted during that minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00151.gz\n",
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      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00153.gz\n",
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00154.gz\n",
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00155.gz\n",
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00156.gz\n",
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00157.gz\n",
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00158.gz\n",
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00159.gz\n",
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00160.gz\n",
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00161.gz\n",
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00162.gz\n",
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00163.gz\n",
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00164.gz\n",
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00165.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00166.gz\n",
      "Reading File: /Users/yutingliao/Desktop/INST728 E/Dataset_women'smarch/womensmarch/part-00167.gz\n"
     ]
    }
   ],
   "source": [
    "selectedCrisis = \"Women's March\"\n",
    "\n",
    "# Determine host-specific location of data\n",
    "tweetDirectory = crisisInfo[selectedCrisis][\"directory\"]\n",
    "tweetGlobPath = os.path.sep + os.path.join(\"Users\", \"yutingliao\", \"Desktop\", \"INST728 E\",\n",
    "                             \"Dataset_women'smarch\",tweetDirectory,\"part-00*.gz\")\n",
    "\n",
    "print (\"Reading files from:\", tweetGlobPath)\n",
    "\n",
    "# Dictionary for mapping dates to data\n",
    "frequencyMap = {}\n",
    "\n",
    "# Twitter's time format, for parsing the created_at date\n",
    "timeFormat = \"%a %b %d %H:%M:%S +0000 %Y\"\n",
    "\n",
    "for tweetFilePath in glob.glob(tweetGlobPath):\n",
    "    print (\"Reading File:\", tweetFilePath)\n",
    "\n",
    "    for line in gzip.open(tweetFilePath, 'rb'):\n",
    "\n",
    "        # Convert from bytes to UTF8 string\n",
    "        decoded_line = line.decode(\"utf8\")\n",
    "        \n",
    "        # Try to read tweet JSON into object\n",
    "        tweetObj = None\n",
    "        try:\n",
    "            tweetObj = json.loads(decoded_line)\n",
    "        except json.JSONDecodeError as jde:\n",
    "            print(\"JSON Decode Error:\", decoded_line)\n",
    "            continue\n",
    "\n",
    "        # Deleted status messages and protected status must be skipped\n",
    "        if ( \"delete\" in tweetObj.keys() or \"status_withheld\" in tweetObj.keys() ):\n",
    "            continue\n",
    "\n",
    "        # Try to extract the time of the tweet\n",
    "        try:\n",
    "            currentTime = datetime.datetime.strptime(tweetObj['created_at'], timeFormat)\n",
    "        except:\n",
    "            print(\"Error parsing time on line:\", decoded_line)\n",
    "            raise\n",
    "\n",
    "        # Flatten this tweet's time\n",
    "        currentTime = currentTime.replace(second=0)\n",
    "\n",
    "        # If our frequency map already has this time, use it, otherwise add\n",
    "        extended_list = frequencyMap.get(currentTime, [])\n",
    "        extended_list.append(tweetObj)\n",
    "        frequencyMap[currentTime] = extended_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start Time: 2017-01-17 23:59:00\n",
      "Stop Time: 2017-01-24 23:59:00\n",
      "Processed Times: 10081\n",
      "Processed Tweet Count: 2550293\n"
     ]
    }
   ],
   "source": [
    "# Fill in any gaps\n",
    "times = sorted(frequencyMap.keys())\n",
    "firstTime = times[0]\n",
    "lastTime = times[-1]\n",
    "thisTime = firstTime\n",
    "\n",
    "# We want to look at per-minute data, so we fill in any missing minutes\n",
    "timeIntervalStep = datetime.timedelta(0, 60)    # Time step in seconds\n",
    "while ( thisTime <= lastTime ):\n",
    "\n",
    "    frequencyMap[thisTime] = frequencyMap.get(thisTime, [])\n",
    "        \n",
    "    thisTime = thisTime + timeIntervalStep\n",
    "\n",
    "# Count the number of minutes\n",
    "print (\"Start Time:\", firstTime)\n",
    "print (\"Stop Time:\", lastTime)\n",
    "print (\"Processed Times:\", len(frequencyMap))\n",
    "    \n",
    "# Count all the tweets per minute\n",
    "print (\"Processed Tweet Count:\", np.sum([len(x) for x in frequencyMap.values()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Twitter Timeline \n",
    "\n",
    "To build a timeline of Twitter usage, we can simply plot the number of tweets posted per minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event Time: 2017-01-21 10:00:00\n",
      "Time Frame: 2017-01-17 23:59:00 2017-01-24 23:59:00\n"
     ]
    },
    {
     "data": {
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AQOhISgEAABA6klIAAACEjqQUAAAAoSMpBQAAQOhylpSa2UNm9q2ZfRxV9n9m9qmZzTSz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\nrqJPxJkKkkTvuuta7bRT+v2CMpXKLATRopc9DP6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b3tKz4zN+fP7xGxrklpk0HH74uh6/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XQUvhLpWC134XZBdDaUnUJP+HYierTahVOd2Q2x0bXIMmgYL01+KvPfIrM0e1P/tjsV75ResaNT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\nZWlfKu0uYF9pMyP9o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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22c9f728d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get data about our crisis\n",
    "selectedCrisis = \"Women's March\"\n",
    "\n",
    "crisisMoment = crisisInfo[selectedCrisis][\"time\"] # When did it occur by epoch time\n",
    "crisisTime = datetime.datetime.utcfromtimestamp(crisisMoment) # Convert to datetime\n",
    "crisisTime = crisisTime.replace(second=0) # Flatten to a specific minute\n",
    "\n",
    "# Print converted time\n",
    "print (\"Event Time:\", crisisTime)\n",
    "\n",
    "# Create a new figure in which to plot minute-by-minute tweet count\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11, 8.5)\n",
    "\n",
    "plt.title(\"Tweet Frequency\")\n",
    "\n",
    "# Sort the times into an array for future use\n",
    "sortedTimes = sorted(frequencyMap.keys())\n",
    "\n",
    "# What time span do these tweets cover?\n",
    "print (\"Time Frame:\", sortedTimes[0], sortedTimes[-1])\n",
    "\n",
    "# Get a count of tweets per minute\n",
    "postFreqList = [len(frequencyMap[x]) for x in sortedTimes]\n",
    "\n",
    "# We'll have ticks every few minutes (more clutters the graph)\n",
    "smallerXTicks = range(0, len(sortedTimes), 30)\n",
    "plt.xticks(smallerXTicks, [sortedTimes[x] for x in smallerXTicks], rotation=90)\n",
    "\n",
    "# Plot the post frequency\n",
    "ax.plot(range(len(frequencyMap)), postFreqList, color=\"blue\", label=\"Posts\")\n",
    "\n",
    "# Get the index for when our crisis occurred\n",
    "crisisXCoord = sortedTimes.index(crisisTime)\n",
    "ax.scatter([crisisXCoord], [np.mean(postFreqList)], c=\"r\", marker=\"x\", s=200, label=\"Event\")\n",
    "\n",
    "ax.grid(b=True, which=u'major')\n",
    "ax.legend()\n",
    "\n",
    "ax.set_xlabel(\"Post Date\")\n",
    "ax.set_ylabel(\"Twitter Frequency\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zooming in on an Event\n",
    "\n",
    "\n",
    "Let's zoom in on who was tweeting about this event as well and what they are saying:\n",
    "\n",
    "- Top users\n",
    "- Top hash tags\n",
    "- Top URLs\n",
    "- Most retweeted tweet\n",
    "\n",
    "For each question, let's look between generally popular and relevant tweets. \"Relevance\" comes in several forms though: temporal (posted around the same time), textual (mentions the event), social (mentions someone who experienced the event), or spatial (happened in/mentions the same place).\n",
    "\n",
    "We'll first look at tweets that happened right after the event."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# List of relevant tweet times\n",
    "after_event_times = []\n",
    "\n",
    "relevant_time_span = 60 # Let's look at the first few minutes after the event\n",
    "\n",
    "current_time_span = 0\n",
    "for current_time in sortedTimes:\n",
    "    if ( current_time >= crisisTime ):\n",
    "        after_event_times.append(current_time)\n",
    "        current_time_span += 1\n",
    "        \n",
    "    if ( current_time_span > relevant_time_span ):\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qm266CYCrr76a888/n/Hjx3PDDTdw1VVX0dHRAcDXvva1qv0cIiIiIuVILHya2RDgEWBw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\nRgkTJsRw+9vfxrD/2GOdDbtv3hzndSds3RpfZ1rpng2zfV9fecNns12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Mb57ewucb3wh/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22c7d47ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new figure in which to plot minute-by-minute tweet count\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11, 8.5)\n",
    "\n",
    "plt.title(\"Temporally Relevant Tweet Frequency\")\n",
    "\n",
    "# Get a count of tweets per minute\n",
    "postFreqList = [len(frequencyMap[x]) for x in after_event_times]\n",
    "\n",
    "# We'll have ticks every few minutes (more clutters the graph)\n",
    "smallerXTicks = range(0, len(after_event_times), 5)\n",
    "plt.xticks(smallerXTicks, [after_event_times[x] for x in smallerXTicks], rotation=90)\n",
    "\n",
    "# Plot the post frequency\n",
    "ax.plot(range(len(after_event_times)), postFreqList, color=\"blue\", label=\"Posts\")\n",
    "\n",
    "ax.grid(b=True, which=u'major')\n",
    "ax.legend()\n",
    "\n",
    "ax.set_xlabel(\"Post Date\")\n",
    "ax.set_ylabel(\"Twitter Frequency\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What were people saying during this time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweet Count: 13770\n"
     ]
    }
   ],
   "source": [
    "temporally_relevant_tweets = [tweet \n",
    "                              for rel_time in after_event_times \n",
    "                              for tweet in frequencyMap[rel_time]]\n",
    "\n",
    "print(\"Tweet Count:\", len(temporally_relevant_tweets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Token Count: 83627\n",
      "Unique Token Count: 56953\n",
      "\n",
      "Frequent Tokens:\n",
      "الله 278\n",
      "اللهم 257\n",
      "170121 143\n",
      "trump 129\n",
      "love 125\n",
      "like 116\n",
      "5555555555 113\n",
      "#iheartawards 111\n",
      "#bestfanarmy 87\n",
      "يارب 79\n",
      "2017 79\n",
      "today 79\n",
      "https 76\n",
      "time 75\n",
      "people 72\n",
      "para 72\n",
      "good 71\n",
      "video 67\n",
      "@youtube 59\n",
      "سبحان 57\n",
      "life 57\n",
      "want 55\n",
      "know 52\n",
      "world 51\n",
      "mais 49\n",
      "free 49\n",
      "أعوذ 49\n",
      "girl 48\n",
      "قلبي 46\n",
      "العظيم 46\n",
      "need 45\n",
      "think 45\n",
      "never 44\n",
      "first 44\n",
      "بعدد 43\n",
      "back 42\n",
      "look 42\n",
      "right 41\n",
      "thanks 41\n",
      "live 41\n",
      "come 41\n",
      "pour 41\n",
      "follow 41\n",
      "الدنيا 40\n",
      "best 40\n",
      "عذاب 39\n",
      "obama 39\n",
      "#now2016 39\n",
      "como 39\n",
      "happy 38\n"
     ]
    }
   ],
   "source": [
    "tokenizer = TweetTokenizer()\n",
    "stops = stopwords.words(\"english\")\n",
    "\n",
    "# This list comprehension iterates through the tweet_list list, and for each\n",
    "#  tweet, it tokenizes the tweet text\n",
    "tokens = [\n",
    "        token.lower() \n",
    "         for tweet in temporally_relevant_tweets \n",
    "             for token in tokenizer.tokenize(tweet[\"text\"])\n",
    "        ]\n",
    "tokens = list(filter(lambda x: x not in stops and len(x) > 3, tokens))\n",
    "\n",
    "print(\"Total Token Count:\", len(tokens))\n",
    "print(\"Unique Token Count:\", len(set(tokens)))\n",
    "\n",
    "tokens_freq = nltk.FreqDist(tokens)\n",
    "\n",
    "print(\"\\nFrequent Tokens:\")\n",
    "for token, count in tokens_freq.most_common(50):\n",
    "    print(token, count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Hashtag Count: 5617\n",
      "Unique Hashtag Count: 3768\n",
      "\n",
      "Frequent Hashtags:\n",
      "iheartawards 111\n",
      "bestfanarmy 87\n",
      "now2016 39\n",
      "세븐틴 36\n",
      "aldubourhappiness 35\n",
      "littlemonsters 34\n",
      "bestmusicvideo 27\n",
      "inauguration 20\n",
      "محاصره_ارهابيين_بجده 18\n",
      "lovatics 17\n",
      "nowladygaga 16\n",
      "superbabysjourney 15\n",
      "harmonizers 15\n",
      "seventeen 15\n",
      "nowplaying 14\n",
      "아이오아이 14\n",
      "bts 13\n",
      "bigolive 13\n",
      "사설토토추천사이트 13\n",
      "rtした人全員フォローする 12\n"
     ]
    }
   ],
   "source": [
    "# This list comprehension iterates through the tweet_list list, and for each\n",
    "#  tweet, it iterates through the hashtags list\n",
    "htags = [\n",
    "        hashtag[\"text\"].lower() \n",
    "         for tweet in temporally_relevant_tweets \n",
    "             for hashtag in tweet[\"entities\"][\"hashtags\"]\n",
    "        ]\n",
    "\n",
    "print(\"Total Hashtag Count:\", len(htags))\n",
    "print(\"Unique Hashtag Count:\", len(set(htags)))\n",
    "\n",
    "htags_freq = nltk.FreqDist(htags)\n",
    "\n",
    "print(\"\\nFrequent Hashtags:\")\n",
    "for tag, count in htags_freq.most_common(20):\n",
    "    print(tag, count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Author Count: 13770\n",
      "Unique Author Count: 13649\n",
      "\n",
      "Active Users:\n",
      "mzawan1 3\n",
      "marichubatang1 3\n",
      "jmohadreda 3\n",
      "destiny041772 3\n",
      "lotekzttus86jd1 3\n",
      "rhiymez 3\n",
      "ringknightsaki 2\n",
      "ilymycal 2\n",
      "paoloigna1 2\n",
      "shnwnnnnhh 2\n",
      "helloqtie 2\n",
      "mwki0wcyj8zhlzp 2\n",
      "satiys18 2\n",
      "ccsworld21a 2\n",
      "dolove_thdus 2\n",
      "roslynksh 2\n",
      "sarahwright323 2\n",
      "aciream23 2\n",
      "cgalgale 2\n",
      "gaelcancelxx 2\n"
     ]
    }
   ],
   "source": [
    "# This list comprehension iterates through the tweet_list list, and for each\n",
    "#  tweet, it iterates through the author list\n",
    "authors = [tweet[\"user\"][\"screen_name\"].lower() for tweet in temporally_relevant_tweets]\n",
    "\n",
    "print(\"Total Author Count:\", len(authors))\n",
    "print(\"Unique Author Count:\", len(set(authors)))\n",
    "\n",
    "author_freq = nltk.FreqDist(authors)\n",
    "\n",
    "print(\"\\nActive Users:\")\n",
    "for author, count in author_freq.most_common(20):\n",
    "    print(author, count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total URL Count: 4219\n",
      "Unique URL Count: 2807\n",
      "\n",
      "Common URLs:\n",
      "None 753\n",
      "http://du3a.org 244\n",
      "http://d3waapp.org/ 91\n",
      "http://ghared.com 61\n",
      "http://7asnat.com/ 41\n",
      "http://zad-muslim.com 27\n",
      "http://bit.ly/2jJtpA6 25\n",
      "http://fllwrs.com 20\n",
      "http://bit.ly/2kbAYfQ 16\n",
      "http://www.totobet1.com 14\n",
      "http://bit.ly/2iWguH3 10\n",
      "http://nico.ms/sm30474818 7\n",
      "http://bnent.jp/optw/ 7\n",
      "http://nico.ms/sm30475339 6\n",
      "http://bnent.jp/optc-den2e/ 5\n",
      "https://www.instagram.com/p/BPhdztbjHil/ 5\n",
      "http://bnent.jp/optc-kr/ 4\n",
      "https://twitter.com/5SOS/status/753850038707589120 4\n",
      "https://twitter.com/5HVotingStats/status/816637289761796096 3\n",
      "http://gharred.net/GharredChannels.aspx 3\n"
     ]
    }
   ],
   "source": [
    "# This list comprehension iterates through the tweet_list list, and for each\n",
    "#  tweet, it iterates through the URL list\n",
    "urls = [\n",
    "        url[\"expanded_url\"]\n",
    "         for tweet in temporally_relevant_tweets \n",
    "             for url in tweet[\"entities\"][\"urls\"]\n",
    "        ]\n",
    "\n",
    "print(\"Total URL Count:\", len(urls))\n",
    "print(\"Unique URL Count:\", len(set(urls)))\n",
    "\n",
    "urls_freq = nltk.FreqDist(urls)\n",
    "\n",
    "print(\"\\nCommon URLs:\")\n",
    "for url, count in urls_freq.most_common(20):\n",
    "    print(url, count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most retweeted tweet AFTER event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retweet Count: 5817\n",
      "Recent Retweet Count: 1608\n",
      "822748951371653120 4881\n",
      "\tTweet: RT @YukikazeIku: https://t.co/DWCvYu8mjW\n",
      "\n",
      "822745591188832256 3791\n",
      "\tTweet: RT @_amatsuki_: 【動画】妄想感傷代償連盟 を 歌ってみた 【96猫&amp;天月】 https://t.co/nAMoyN2bbC を投稿しました。 #sm30475339\n",
      "\n",
      "822749885502001152 3159\n",
      "\tTweet: RT @Geenius_B: 도경수 너무잘하잖아 ㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋㅋ https://t.co/2lY7yRMwer\n",
      "\n",
      "822755081225314305 2614\n",
      "\tTweet: RT @takoyaki515: ※同一人物です https://t.co/BZROrOVaHc\n",
      "\n",
      "822759179857240066 2411\n",
      "\tTweet: RT @ILR_Rimu: 全巻購入特典！？ https://t.co/8AGTJ92BqM\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Find retweets\n",
    "retweets = list(filter(lambda x: \"retweeted_status\" in x, temporally_relevant_tweets))\n",
    "print(\"Retweet Count:\", len(retweets))\n",
    "\n",
    "# But we also want to filter out retweets whose original tweet\n",
    "#  was posted BEFORE our event\n",
    "retweets = list(filter(\n",
    "    lambda tweet: crisisTime <= datetime.datetime.strptime(tweet[\"retweeted_status\"][\"created_at\"], timeFormat),\n",
    "    retweets\n",
    "))\n",
    "print(\"Recent Retweet Count:\", len(retweets))\n",
    "\n",
    "# For each retweet, get its pair\n",
    "rt_pairs = [(tweet[\"retweeted_status\"][\"id\"], tweet[\"retweeted_status\"][\"retweet_count\"]) \n",
    "          for tweet in retweets]\n",
    "\n",
    "# Get the unique tweet IDs\n",
    "uniq_ids = set([x[0] for x in rt_pairs])\n",
    "\n",
    "# Map retweet IDs to their counts\n",
    "rt_map = {}\n",
    "for local_id in uniq_ids:\n",
    "    max_rt_count = max([x[1] for x in filter(lambda x: x[0] == local_id, rt_pairs)])\n",
    "    rt_map[local_id] = max_rt_count\n",
    "    \n",
    "# Sort the retweets by count\n",
    "top_5_rts = sorted(rt_map, key=rt_map.get, reverse=True)[:5]\n",
    "\n",
    "# For each of the top 5, print them\n",
    "for rt in top_5_rts:\n",
    "    \n",
    "    tweet = list(filter(lambda x: x[\"retweeted_status\"][\"id\"] == rt, retweets))[0]\n",
    "    \n",
    "    print(rt, rt_map[rt])\n",
    "    print(\"\\tTweet:\", tweet[\"text\"])\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Event Detection w/ Keyword Frequency\n",
    "\n",
    "Twitter is good for breaking news. When an impactful event occurs, we often see a spike on Twitter of the usage of a related keyword. Some examples are below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kVMvJ4JPiU+cGNhxlYvR4LKuIvL1quSmTsvuUt9G17KFldVh92IOdVeGvcs3x2uevpatx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22c9d2ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What keywords are we interested in?\n",
    "targetKeywords = crisisInfo[selectedCrisis][\"keywords\"]\n",
    "\n",
    "# Build an empty map for each keyword we are seaching for\n",
    "targetCounts = {x:[] for x in targetKeywords}\n",
    "totalCount = []\n",
    "\n",
    "# For each minute, pull the tweet text and search for the keywords we want\n",
    "for t in sortedTimes:\n",
    "    timeObj = frequencyMap[t]\n",
    "    \n",
    "    # Temporary counter for this minute\n",
    "    localTargetCounts = {x:0 for x in targetKeywords}\n",
    "    localTotalCount = 0\n",
    "    \n",
    "    for tweetObj in timeObj:\n",
    "        tweetString = tweetObj[\"text\"].lower()\n",
    "\n",
    "        localTotalCount += 1\n",
    "        \n",
    "        # Add to the counter if the target keyword is in this tweet\n",
    "        for keyword in targetKeywords:\n",
    "            if ( keyword in tweetString ):\n",
    "                localTargetCounts[keyword] += 1\n",
    "                \n",
    "    # Add the counts for this minute to the main counter\n",
    "    totalCount.append(localTotalCount)\n",
    "    for keyword in targetKeywords:\n",
    "        targetCounts[keyword].append(localTargetCounts[keyword])\n",
    "        \n",
    "# Now plot the total frequency and frequency of each keyword\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11, 8.5)\n",
    "\n",
    "# Set title\n",
    "plt.title(\"Tweet Frequency\")\n",
    "\n",
    "# ticks interval\n",
    "smallerXTicks = range(0, len(sortedTimes), 15)\n",
    "plt.xticks(smallerXTicks, [sortedTimes[x] for x in smallerXTicks], rotation=90)\n",
    "\n",
    "# Plot in log-scale because overall tweet volume is much higher than relevant volume\n",
    "ax.semilogy(range(len(frequencyMap)), totalCount, label=\"Total\")\n",
    "\n",
    "# Plot a simple X where the disaster occurred\n",
    "ax.scatter([crisisXCoord], [100], c=\"r\", marker=\"x\", s=100, label=\"Crisis\")\n",
    "\n",
    "# For each keyword in our target keywords...\n",
    "for keyword in targetKeywords:\n",
    "    \n",
    "    # Print the target count for each keyword and time\n",
    "    ax.semilogy(range(len(frequencyMap)), targetCounts[keyword], label=keyword)\n",
    "    \n",
    "# Legend and titles\n",
    "ax.set_xlabel(\"Post Date\")\n",
    "ax.set_ylabel(\"Log(Frequency)\")\n",
    "ax.legend()\n",
    "ax.grid(b=True, which=u'major')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zooming in on textually relevant tweets\n",
    "\n",
    "We've looked at tweets that happened right after the event, now let's look at tweets that mention the event."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keyword: women's march Count: 2006\n",
      "Keyword: resist Count: 1322\n",
      "Keyword: notmypresident Count: 120\n",
      "Keyword: demonstration Count: 117\n",
      "Keyword: women's right Count: 521\n",
      "Keyword: human right Count: 298\n",
      "Keyword: planned parenthood Count: 310\n"
     ]
    }
   ],
   "source": [
    "relevant_keyword_tweets = {}\n",
    "\n",
    "for rel_keyword in targetKeywords:\n",
    "    relevant_tweets = [tweet \n",
    "                       for this_time in \n",
    "                           filter(lambda x: x >= crisisTime, sortedTimes) \n",
    "                       for tweet in \n",
    "                           filter(lambda y: rel_keyword in y[\"text\"].lower(), \n",
    "                                  frequencyMap[this_time])\n",
    "                      ]\n",
    "    \n",
    "    relevant_keyword_tweets[rel_keyword] = relevant_tweets\n",
    "    print(\"Keyword:\", rel_keyword, \"Count:\", len(relevant_tweets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "------------------------\n",
      "Keyword: women's march Count: 2006\n",
      "Total Hashtag Count: 386\n",
      "Unique Hashtag Count: 183\n",
      "\n",
      "Frequent Hashtags:\n",
      "womensmarch 136\n",
      "breakingnews 12\n",
      "whyimarch 8\n",
      "lovatics 7\n",
      "bestfanarmy 5\n",
      "iheartawards 5\n",
      "indianapolis 4\n",
      "indianastatehouse 4\n",
      "breaking 4\n",
      "lovetrumpshate 3\n",
      "women 3\n",
      "news 3\n",
      "politics 3\n",
      "yeg 3\n",
      "ableg 3\n",
      "womensmarchonwashington 2\n",
      "9news 2\n",
      "toronto 2\n",
      "periscope 2\n",
      "theresistance 2\n",
      "\n",
      "Total Author Count: 2006\n",
      "Unique Author Count: 2000\n",
      "\n",
      "Active Users:\n",
      "michaelawoodjr 2\n",
      "rxssaaa_16 2\n",
      "socincolosprgs 2\n",
      "camillendavis1 2\n",
      "tyjw_ 2\n",
      "rukannur 2\n",
      "alleyezonyou_ 1\n",
      "joiebutter 1\n",
      "tp_englenton 1\n",
      "beckywhin 1\n",
      "chicagonewsnow 1\n",
      "sergisuarezm 1\n",
      "judith2000641 1\n",
      "jesss_darcy 1\n",
      "dailyoutfit 1\n",
      "femdem2021 1\n",
      "unity_humanity 1\n",
      "dmckeirnan 1\n",
      "rarechromomum 1\n",
      "kellytyrpak 1\n",
      "Total URL Count: 926\n",
      "Unique URL Count: 551\n",
      "\n",
      "Common URLs:\n",
      "None 255\n",
      "http://abcn.ws/2kcdI1n 12\n",
      "http://abc7.la/2jjMnKl 10\n",
      "http://www.cnn.com/2017/01/21/politics/womens-march-donald-trump-inauguration-sizes/index.html 8\n",
      "http://thkpr.gs/9e6b47dc21bd 8\n",
      "https://twitter.com/i/moments/822581105459167235 6\n",
      "https://twitter.com/i/moments/822803803049078784 6\n",
      "http://nyti.ms/2jjwmUV 6\n",
      "http://www.nylon.com/articles/lauren-jauregui-interview?utm_source=twitter&utm_medium=social&utm_campaign=nylon 5\n",
      "http://nytlive.nytimes.com/womenintheworld/2017/01/20/billionaire-george-soros-has-ties-to-more-than-50-partners-of-the-womens-march-on-washington/ 4\n",
      "http://hill.cm/AQpDjiB 4\n",
      "http://hill.cm/3PCpR6g 4\n",
      "http://www.FindTheThug.com 4\n",
      "http://www.vox.com/2017/1/22/14350808/womens-marches-largest-demonstration-us-history-map 4\n",
      "https://twitter.com/realDonaldTrump/status/823937936056008704 4\n",
      "https://www.theguardian.com/lifeandstyle/live/2017/jan/21/womens-march-on-washington-and-other-anti-trump-protests-around-the-world-live-coverage?CMP=share_btn_tw 3\n",
      "https://www.theguardian.com/world/2017/jan/20/womens-march-canada-protesters-denied-entry-us?CMP=share_btn_tw 3\n",
      "http://n.pr/2kcnFfl 3\n",
      "http://huff.to/2jMc4Hh 3\n",
      "http://bit.ly/2j8xehC 3\n",
      "\n",
      "------------------------\n",
      "Keyword: resist Count: 1322\n",
      "Total Hashtag Count: 1696\n",
      "Unique Hashtag Count: 483\n",
      "\n",
      "Frequent Hashtags:\n",
      "theresistance 285\n",
      "womensmarch 224\n",
      "resist 115\n",
      "resistance 74\n",
      "trumpleaks 49\n",
      "resisttrump 44\n",
      "resisttrumptuesdays 27\n",
      "trump 26\n",
      "notmypresident 25\n",
      "womensmarchonwashington 21\n",
      "indivisible 20\n",
      "breaking 16\n",
      "alternativefacts 16\n",
      "resistfromday1 14\n",
      "bestfanarmy 14\n",
      "iheartawards 14\n",
      "lovatics 13\n",
      "inauguration 12\n",
      "maga 9\n",
      "impeachtrump 9\n",
      "\n",
      "Total Author Count: 1322\n",
      "Unique Author Count: 1303\n",
      "\n",
      "Active Users:\n",
      "caminahotx 11\n",
      "milt_pat 2\n",
      "otono60 2\n",
      "tinytara310 2\n",
      "lieztche 2\n",
      "lycoris_aurea 2\n",
      "bsfil 2\n",
      "callawaybot 2\n",
      "aroseblush 2\n",
      "peedoffmom 2\n",
      "forevermyangel2 1\n",
      "protestwatch 1\n",
      "jeffe04 1\n",
      "herondvlerunes 1\n",
      "kbshroff 1\n",
      "kage_fukukaicho 1\n",
      "jay_ab81 1\n",
      "dangusset 1\n",
      "bhavesh_yuva 1\n",
      "hannahblue3 1\n",
      "Total URL Count: 605\n",
      "Unique URL Count: 371\n",
      "\n",
      "Common URLs:\n",
      "None 212\n",
      "http://www.rawstory.com/2017/01/the-womens-march-on-washington-will-have-sister-marches-in-over-75-countries/ 10\n",
      "https://twitter.com/CNBC/status/823898867880067072 5\n",
      "https://www.theguardian.com/commentisfree/2017/jan/21/womens-march-donald-trump-inauguration?CMP=share_btn_tw 2\n",
      "https://www.hackread.com/radio-station-hacked-with-f-donald-trump-song/ 2\n",
      "http://wapo.st/2kd1jtY 2\n",
      "https://twitter.com/cher/status/822964158601822209 2\n",
      "https://twitter.com/CharlesMBlow/status/823305360387743744 2\n",
      "https://twitter.com/i/moments/823284010117804033 2\n",
      "http://whatdoidoabouttrump.com 2\n",
      "https://www.not-up-for-grabs.org/take-action 2\n",
      "https://www.theguardian.com/commentisfree/2017/jan/23/britain-for-europe-remainers-hard-brexit?CMP=share_btn_tw 2\n",
      "https://twitter.com/jaketapper/status/823700047938338816 2\n",
      "http://prt.st/N9HlMQ 1\n",
      "http://flip.it/gG6iZ7 1\n",
      "http://bit.ly/2k3mPRX 1\n",
      "https://twitter.com/trustnorminah/status/822551242530521088 1\n",
      "https://twitter.com/JamesPMorrison/status/822421949246623745 1\n",
      "https://twitter.com/JoyAnnReid/status/822705408116662272 1\n",
      "http://bit.ly/2k9IYlA 1\n",
      "\n",
      "------------------------\n",
      "Keyword: notmypresident Count: 120\n",
      "Total Hashtag Count: 319\n",
      "Unique Hashtag Count: 122\n",
      "\n",
      "Frequent Hashtags:\n",
      "notmypresident 111\n",
      "womensmarch 22\n",
      "theresistance 12\n",
      "inauguration 7\n",
      "resist 6\n",
      "fucktrump 6\n",
      "resisttrump 5\n",
      "trump 5\n",
      "nevertrump 5\n",
      "notmypresidenttrump 5\n",
      "nevermypresident 4\n",
      "trumpleaks 4\n",
      "womensmarchonwashington 4\n",
      "donaldtrump 2\n",
      "theresistanse 2\n",
      "werestillhere 2\n",
      "fightlikehell 2\n",
      "whyimarch 2\n",
      "boswomensmarch 2\n",
      "spicerfacts 2\n",
      "\n",
      "Total Author Count: 120\n",
      "Unique Author Count: 120\n",
      "\n",
      "Active Users:\n",
      "forevermyangel2 1\n",
      "protestwatch 1\n",
      "hannahblue3 1\n",
      "ajbiondo77 1\n",
      "lexie11111 1\n",
      "beadyknitknut 1\n",
      "u_thought_it2 1\n",
      "newtguinea 1\n",
      "katrinkristiina 1\n",
      "leeap52 1\n",
      "jpmahoneyy 1\n",
      "jasonelek 1\n",
      "aprylmiller26 1\n",
      "evagreenweb 1\n",
      "sarah_singhs 1\n",
      "nict10 1\n",
      "resa_two 1\n",
      "bobschecter 1\n",
      "brohkenmemories 1\n",
      "cbug_thinmint 1\n",
      "Total URL Count: 44\n",
      "Unique URL Count: 33\n",
      "\n",
      "Common URLs:\n",
      "None 12\n",
      "http://prt.st/N9HlMQ 1\n",
      "https://twitter.com/SortaBad/status/822526142645080068 1\n",
      "https://twitter.com/i/web/status/822802487732211712 1\n",
      "https://twitter.com/i/web/status/822828773427056641 1\n",
      "https://twitter.com/NBCNews/status/822825987956056065 1\n",
      "https://www.instagram.com/p/BPiRMi-Bz1w/ 1\n",
      "https://www.instagram.com/p/BPijq9PhGxl/ 1\n",
      "https://www.instagram.com/p/BPisA3cgX8r/ 1\n",
      "https://twitter.com/thehill/status/822917193641947140 1\n",
      "http://ow.ly/1223308dZXE 1\n",
      "https://twitter.com/theonlyadult/status/822926543651540992 1\n",
      "https://twitter.com/larryryckman/status/822849232323252225 1\n",
      "https://twitter.com/luke_kerr/status/822851832653312001 1\n",
      "https://www.instagram.com/p/BPjA-viBqML/ 1\n",
      "https://www.instagram.com/p/BPjITOeALx-/ 1\n",
      "https://twitter.com/rascouet/status/823035518313267202 1\n",
      "https://www.instagram.com/p/BPj7D9CDC1A/ 1\n",
      "https://nyti.ms/2jLMNfL 1\n",
      "https://twitter.com/mcuban/status/822983742444752896 1\n",
      "\n",
      "------------------------\n",
      "Keyword: demonstration Count: 117\n",
      "Total Hashtag Count: 36\n",
      "Unique Hashtag Count: 19\n",
      "\n",
      "Frequent Hashtags:\n",
      "womensmarch 13\n",
      "trump 4\n",
      "usa 2\n",
      "soros 2\n",
      "jallikattu 1\n",
      "justiceforjallikattu 1\n",
      "womansmarch 1\n",
      "demonstrations 1\n",
      "democrats 1\n",
      "libertarian 1\n",
      "hppoint 1\n",
      "wearewatchingyou 1\n",
      "buckleupbuttercup 1\n",
      "oakland 1\n",
      "ottawa 1\n",
      "ottnews 1\n",
      "peace 1\n",
      "dresden 1\n",
      "nodapl 1\n",
      "\n",
      "Total Author Count: 117\n",
      "Unique Author Count: 117\n",
      "\n",
      "Active Users:\n",
      "tp_englenton 1\n",
      "sergisuarezm 1\n",
      "eliascastillo38 1\n",
      "_thala57 1\n",
      "juliacburns 1\n",
      "soulsis7 1\n",
      "imemilydsouza 1\n",
      "sterlingmholmes 1\n",
      "ebullientworks 1\n",
      "druidnectan 1\n",
      "tamaravitelli 1\n",
      "figdari 1\n",
      "teh_moko 1\n",
      "earthandeconomy 1\n",
      "tokyorav 1\n",
      "mariasilvernai1 1\n",
      "sazdoc 1\n",
      "_karendavilaa 1\n",
      "columbaaaa 1\n",
      "gerrypilgrim 1\n",
      "Total URL Count: 42\n",
      "Unique URL Count: 23\n",
      "\n",
      "Common URLs:\n",
      "None 10\n",
      "http://www.vox.com/2017/1/22/14350808/womens-marches-largest-demonstration-us-history-map 5\n",
      "http://bbc.in/2j7gAil 4\n",
      "http://bit.ly/2jQb9Fm 3\n",
      "http://econ.st/2iKOxqc 2\n",
      "http://680.azzrol.pw 1\n",
      "https://twitter.com/i/web/status/822863233824550917 1\n",
      "http://viid.me/qdhSGV 1\n",
      "http://www.globalecho.org/66387/usa-massen-demonstrationen-gegen-donald-trump/ 1\n",
      "https://www.instagram.com/p/BPjmmavgDv7/ 1\n",
      "http://cbsn.ws/2jMOsCd 1\n",
      "http://www.epochtimes.de/politik/welt/georg-soros-und-eine-zufaellige-spontane-weltweite-frauendemonstration-gegen-trump-a2030102.html?tweet=1 1\n",
      "https://apple.news/AWHFvWXR5QP6j5kd9H5y8mg 1\n",
      "http://luc.me/ff 1\n",
      "http://llir.biz/pz?NUMA 1\n",
      "https://twitter.com/i/web/status/823338687547117568 1\n",
      "http://bit.ly/2kkY3wG 1\n",
      "http://ow.ly/nLNQ308hsZg 1\n",
      "https://twitter.com/i/web/status/823669077080240129 1\n",
      "http://bit.ly/1omvaSe 1\n",
      "\n",
      "------------------------\n",
      "Keyword: women's right Count: 521\n",
      "Total Hashtag Count: 190\n",
      "Unique Hashtag Count: 50\n",
      "\n",
      "Frequent Hashtags:\n",
      "womensmarch 112\n",
      "whyimarch 9\n",
      "womensmarchlondon 8\n",
      "equalityforall 4\n",
      "womensmarchberlin 3\n",
      "womansmarch 3\n",
      "berlin 2\n",
      "womensmarchonwashington 2\n",
      "tahanimansour 2\n",
      "whywemarch 2\n",
      "sf 2\n",
      "womensrights 2\n",
      "girlpower 2\n",
      "theresistance 1\n",
      "womenwhohaveinspiredme 1\n",
      "dublin 1\n",
      "run4allwomen 1\n",
      "equalrights 1\n",
      "district5 1\n",
      "womensmarchatlanta 1\n",
      "\n",
      "Total Author Count: 521\n",
      "Unique Author Count: 521\n",
      "\n",
      "Active Users:\n",
      "layden_tw 1\n",
      "laurakatebarrow 1\n",
      "purna_sen 1\n",
      "katyptv 1\n",
      "sammijade06 1\n",
      "elinorhs 1\n",
      "leostreet2 1\n",
      "milt_pat 1\n",
      "danaks20 1\n",
      "ethicsprofky 1\n",
      "luci3020 1\n",
      "soaprookie 1\n",
      "darkmattersproj 1\n",
      "emmakdavis18 1\n",
      "reds_2001 1\n",
      "feministrocks 1\n",
      "droppedsync_lm 1\n",
      "dementedbonxie 1\n",
      "mcsheehyshannon 1\n",
      "nycmargie 1\n",
      "Total URL Count: 221\n",
      "Unique URL Count: 29\n",
      "\n",
      "Common URLs:\n",
      "None 186\n",
      "http://ow.ly/qOls308d2b7 6\n",
      "https://www.instagram.com/p/BPi9m0qhica/ 3\n",
      "https://twitter.com/i/web/status/822825698993782784 1\n",
      "http://cnn.it/2j7gIy6 1\n",
      "http://ift.tt/2jKhaTH 1\n",
      "https://twitter.com/i/web/status/822830868729331712 1\n",
      "https://twitter.com/sadiqkhan/status/822765805775581185....we 1\n",
      "https://twitter.com/mike_spazzin/status/822863282197368832 1\n",
      "https://www.instagram.com/p/BPiekHQgRD4/ 1\n",
      "https://twitter.com/taylorswift13/status/652592357196480512 1\n",
      "https://www.instagram.com/p/BPirOKbA-4s/ 1\n",
      "http://www.nbcdfw.com/news/local/Saturdays-Dallas-Womens-March-Part-of-National-Voice-for-Womens-Rights-Social-Justice-411363355.html 1\n",
      "https://twitter.com/i/web/status/822929591903784960 1\n",
      "https://twitter.com/mellichrous/status/822926108626743296 1\n",
      "http://twb.ly/hp1HEG 1\n",
      "https://twitter.com/oopsadaisy94/status/822930767516991488 1\n",
      "http://youtube.com/watch?v=4AafOhLUMvc 1\n",
      "https://twitter.com/i/web/status/823003399071240192 1\n",
      "https://twitter.com/i/web/status/823033019262976000 1\n",
      "\n",
      "------------------------\n",
      "Keyword: human right Count: 298\n",
      "Total Hashtag Count: 172\n",
      "Unique Hashtag Count: 71\n",
      "\n",
      "Frequent Hashtags:\n",
      "womensmarch 62\n",
      "whyimarch 13\n",
      "markisatool 8\n",
      "istandwithshane 6\n",
      "nastywoman 4\n",
      "globalgag 4\n",
      "hungary 3\n",
      "whywemarch 3\n",
      "nycwomensmarch 3\n",
      "end_yemen_siege 3\n",
      "equalrights 2\n",
      "womensmarchlosangeles 2\n",
      "womenmarchonwashington 1\n",
      "leyblasfemia 1\n",
      "blasphemylaw 1\n",
      "nelsonmandela 1\n",
      "wtf 1\n",
      "equalpay 1\n",
      "women 1\n",
      "womenmobilizenc 1\n",
      "\n",
      "Total Author Count: 298\n",
      "Unique Author Count: 298\n",
      "\n",
      "Active Users:\n",
      "dumbtrump3 1\n",
      "eenymeenykids 1\n",
      "blasanqui 1\n",
      "laurakatebarrow 1\n",
      "theresehirst 1\n",
      "fifthorgajsms 1\n",
      "michellegrey9 1\n",
      "sammijade06 1\n",
      "peace20170115_3 1\n",
      "yvette_hynes 1\n",
      "jennrigg 1\n",
      "danaks20 1\n",
      "ethicsprofky 1\n",
      "luci3020 1\n",
      "darkmattersproj 1\n",
      "orniastic 1\n",
      "d3d0c3d 1\n",
      "zero_gov 1\n",
      "reds_2001 1\n",
      "whirligig777 1\n",
      "Total URL Count: 119\n",
      "Unique URL Count: 53\n",
      "\n",
      "Common URLs:\n",
      "None 63\n",
      "https://www.youtube.com/markiplier/live 3\n",
      "http://www.YouTube.com/markiplier/live 2\n",
      "https://youtu.be/quAQ_S9XSfk 2\n",
      "http://fb.me/3Lu4Vvpa2 1\n",
      "http://www.citizengo.org/hazteoir/pr/40109-stop-ley-blasfemia?tc=tw&tcid=31733386 1\n",
      "http://ln.is/blogspot.co.uk/drDgT 1\n",
      "https://twitter.com/i/web/status/822848675407863809 1\n",
      "http://cnn.it/2kdKnr9 1\n",
      "https://twitter.com/i/web/status/822860843088015363 1\n",
      "https://twitter.com/TabithaLipkin/status/822867108237185026 1\n",
      "https://twitter.com/i/web/status/822875753830289410 1\n",
      "http://ow.ly/QzJ6308dKM7 1\n",
      "https://twitter.com/i/web/status/822879348327792640 1\n",
      "https://www.instagram.com/p/BPibWOhg3J4/ 1\n",
      "https://youtu.be/16Z2rfYh_0w 1\n",
      "https://www.instagram.com/p/BPiekHQgRD4/ 1\n",
      "https://twitter.com/i/web/status/822900596701204480 1\n",
      "http://trumpisnews.ml/FG 1\n",
      "https://twitter.com/Fatushow/status/822916394631233537 1\n",
      "\n",
      "------------------------\n",
      "Keyword: planned parenthood Count: 310\n",
      "Total Hashtag Count: 40\n",
      "Unique Hashtag Count: 29\n",
      "\n",
      "Frequent Hashtags:\n",
      "womensmarch 8\n",
      "istandwithpp 3\n",
      "amjoy 2\n",
      "youresowhite 2\n",
      "cspan 1\n",
      "follow 1\n",
      "lovenothate 1\n",
      "satan 1\n",
      "georgesoros 1\n",
      "womensmarchla 1\n",
      "flipagram 1\n",
      "knowauthentic 1\n",
      "womenwhohaveinspiredme 1\n",
      "womansmarch 1\n",
      "whyimarch 1\n",
      "icymi 1\n",
      "equality 1\n",
      "specialkmb1969 1\n",
      "kingforaday 1\n",
      "wtf 1\n",
      "\n",
      "Total Author Count: 310\n",
      "Unique Author Count: 310\n",
      "\n",
      "Active Users:\n",
      "haylie_renae__ 1\n",
      "malindofor45th 1\n",
      "vrgoofball 1\n",
      "wtsmithxxx 1\n",
      "preinsko 1\n",
      "camren5tharmony 1\n",
      "fieldproven 1\n",
      "altadivina 1\n",
      "annhwoodnky 1\n",
      "nessescity 1\n",
      "agirldivision 1\n",
      "momofthegoalie 1\n",
      "asagissor 1\n",
      "left_republic 1\n",
      "janjohnsonfl 1\n",
      "chwxtzuyu 1\n",
      "mccurdian_kress 1\n",
      "m0rningw00dy 1\n",
      "over9000pints 1\n",
      "hastings450f4 1\n",
      "Total URL Count: 231\n",
      "Unique URL Count: 41\n",
      "\n",
      "Common URLs:\n",
      "None 180\n",
      "https://www.google.com/amp/s/www.lifesitenews.com/mobile/news/breaking-trump-signs-order-defunding-international-planned-parenthood-forei?client=safari 6\n",
      "https://twitter.com/cnn/status/823464402804494336 6\n",
      "http://bit.ly/2cq5F2B 2\n",
      "http://breaking.newspaper24.us/DqK 1\n",
      "https://twitter.com/i/web/status/822851649156812801 1\n",
      "https://twitter.com/cnsnnslv/status/822885927525711873 1\n",
      "https://twitter.com/huffingtonpost/status/822891299208658946 1\n",
      "https://twitter.com/deniseclepper/status/820684112159617025 1\n",
      "https://flipagram.com/f/12itwo7GrhK 1\n",
      "http://jus.tj/1rit 1\n",
      "http://knowauthentic.com/2017/01/21/scarlett-johannson-shares-her-own-planned-parenthood-story-at-the-womens-march/ 1\n",
      "http://www.teaparty.org/report-trump-defund-planned-parenthood-sunday-214033/#.WIPbv5TuPZ8.twitter 1\n",
      "http://www.huffingtonpost.com/entry/women-plan-massive-run-from-ny-to-dc-to-benefit-planned-parenthood_us_5875067ee4b099cdb0ff86f2?ncid=engmodushpmg00000004 1\n",
      "https://twitter.com/PPact/status/822869770659917825 1\n",
      "http://slate.me/2jlLgdb 1\n",
      "https://www.instagram.com/p/BPlDDjIgrqiIkTRKA0F8AIjViTxxjUHrdWGjWA0/ 1\n",
      "http://GetGumdrop.com 1\n",
      "http://fb.me/1RHriksM2 1\n",
      "http://fb.me/5F6CwPNVF 1\n"
     ]
    }
   ],
   "source": [
    "for target_keyword, textually_relevant_tweets in relevant_keyword_tweets.items():\n",
    "\n",
    "    print(\"\\n------------------------\")\n",
    "    print(\"Keyword:\", target_keyword, \"Count:\", len(textually_relevant_tweets))\n",
    "\n",
    "    # This list comprehension iterates through the tweet_list list, and for each\n",
    "    #  tweet, it iterates through the hashtags list\n",
    "    htags = [\n",
    "            hashtag[\"text\"].lower() \n",
    "             for tweet in textually_relevant_tweets \n",
    "                 for hashtag in tweet[\"entities\"][\"hashtags\"]\n",
    "            ]\n",
    "\n",
    "    print(\"Total Hashtag Count:\", len(htags))\n",
    "    print(\"Unique Hashtag Count:\", len(set(htags)))\n",
    "\n",
    "    htags_freq = nltk.FreqDist(htags)\n",
    "\n",
    "    print(\"\\nFrequent Hashtags:\")\n",
    "    for tag, count in htags_freq.most_common(20):\n",
    "        print(tag, count)\n",
    "\n",
    "    # This list comprehension iterates through the tweet_list list, and for each\n",
    "    #  tweet, it iterates through the author list\n",
    "    authors = [tweet[\"user\"][\"screen_name\"].lower() for tweet in textually_relevant_tweets]\n",
    "\n",
    "    print(\"\\nTotal Author Count:\", len(authors))\n",
    "    print(\"Unique Author Count:\", len(set(authors)))\n",
    "\n",
    "    author_freq = nltk.FreqDist(authors)\n",
    "\n",
    "    print(\"\\nActive Users:\")\n",
    "    for author, count in author_freq.most_common(20):\n",
    "        print(author, count)\n",
    "\n",
    "    # This list comprehension iterates through the tweet_list list, and for each\n",
    "    #  tweet, it iterates through the URL list\n",
    "    urls = [\n",
    "            url[\"expanded_url\"]\n",
    "             for tweet in textually_relevant_tweets \n",
    "                 for url in tweet[\"entities\"][\"urls\"]\n",
    "            ]\n",
    "\n",
    "    print(\"Total URL Count:\", len(urls))\n",
    "    print(\"Unique URL Count:\", len(set(urls)))\n",
    "\n",
    "    urls_freq = nltk.FreqDist(urls)\n",
    "\n",
    "    print(\"\\nCommon URLs:\")\n",
    "    for url, count in urls_freq.most_common(20):\n",
    "        print(url, count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging All Relevant Tweets\n",
    "\n",
    "We looked at tweets that match our relevant keywords. Now let's merge them all together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique textually relevant tweets: 4447\n"
     ]
    }
   ],
   "source": [
    "all_rel_tweets = {tweet[\"id\"]:tweet \n",
    "                  for local_tweet_list in relevant_keyword_tweets.values() \n",
    "                  for tweet in local_tweet_list}\n",
    "\n",
    "print(\"Unique textually relevant tweets:\", len(all_rel_tweets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Token Count: 42901\n",
      "Unique Token Count: 11286\n",
      "\n",
      "Frequent Tokens:\n",
      "women's 2561\n",
      "march 2052\n",
      "rights 1053\n",
      "#womensmarch 533\n",
      "trump 390\n",
      "planned 318\n",
      "human 313\n",
      "washington 309\n",
      "parenthood 303\n",
      "women 295\n",
      "resistance 295\n",
      "#theresistance 285\n",
      "inauguration 242\n",
      "trump's 192\n",
      "today 188\n",
      "marches 186\n",
      "people 171\n",
      "world 151\n",
      "resist 148\n",
      "black 146\n",
      "\n",
      "Total Hashtag Count: 2658\n",
      "Unique Hashtag Count: 783\n",
      "\n",
      "Frequent Hashtags:\n",
      "womensmarch 533\n",
      "theresistance 285\n",
      "resist 115\n",
      "notmypresident 111\n",
      "resistance 74\n",
      "trumpleaks 50\n",
      "resisttrump 44\n",
      "trump 34\n",
      "whyimarch 30\n",
      "womensmarchonwashington 27\n",
      "resisttrumptuesdays 27\n",
      "inauguration 21\n",
      "lovatics 21\n",
      "breaking 20\n",
      "bestfanarmy 20\n",
      "alternativefacts 20\n",
      "indivisible 20\n",
      "iheartawards 19\n",
      "resistfromday1 14\n",
      "breakingnews 12\n",
      "\n",
      "Total Author Count: 4447\n",
      "Unique Author Count: 4409\n",
      "\n",
      "Active Users:\n",
      "caminahotx 11\n",
      "michaelawoodjr 2\n",
      "skyclad_dancer 2\n",
      "abracadabrany 2\n",
      "jamesvicktory 2\n",
      "rxssaaa_16 2\n",
      "paulhaider74 2\n",
      "deeloralei 2\n",
      "aye_veri 2\n",
      "socincolosprgs 2\n",
      "camillendavis1 2\n",
      "tyjw_ 2\n",
      "w_i_uk 2\n",
      "britbwebbhair 2\n",
      "denihanmary 2\n",
      "rukannur 2\n",
      "myradsirois1 2\n",
      "the_anti_fox 2\n",
      "milt_pat 2\n",
      "otono60 2\n",
      "\n",
      "Total URL Count: 2017\n",
      "Unique URL Count: 1067\n",
      "\n",
      "Common URLs:\n",
      "None 777\n",
      "http://abcn.ws/2kcdI1n 12\n",
      "http://abc7.la/2jjMnKl 10\n",
      "http://www.rawstory.com/2017/01/the-womens-march-on-washington-will-have-sister-marches-in-over-75-countries/ 10\n",
      "http://www.cnn.com/2017/01/21/politics/womens-march-donald-trump-inauguration-sizes/index.html 8\n",
      "http://thkpr.gs/9e6b47dc21bd 8\n",
      "https://twitter.com/i/moments/822581105459167235 7\n",
      "https://twitter.com/i/moments/822803803049078784 6\n",
      "http://nyti.ms/2jjwmUV 6\n",
      "http://ow.ly/qOls308d2b7 6\n",
      "https://www.google.com/amp/s/www.lifesitenews.com/mobile/news/breaking-trump-signs-order-defunding-international-planned-parenthood-forei?client=safari 6\n",
      "https://twitter.com/cnn/status/823464402804494336 6\n",
      "http://www.nylon.com/articles/lauren-jauregui-interview?utm_source=twitter&utm_medium=social&utm_campaign=nylon 5\n",
      "http://www.vox.com/2017/1/22/14350808/womens-marches-largest-demonstration-us-history-map 5\n",
      "https://twitter.com/CNBC/status/823898867880067072 5\n",
      "http://nytlive.nytimes.com/womenintheworld/2017/01/20/billionaire-george-soros-has-ties-to-more-than-50-partners-of-the-womens-march-on-washington/ 4\n",
      "http://hill.cm/AQpDjiB 4\n",
      "http://hill.cm/3PCpR6g 4\n",
      "http://www.FindTheThug.com 4\n",
      "https://twitter.com/realDonaldTrump/status/823937936056008704 4\n"
     ]
    }
   ],
   "source": [
    "textually_relevant_tweets = list(all_rel_tweets.values())\n",
    "\n",
    "# This list comprehension iterates through the tweet_list list, and for each\n",
    "#  tweet, it tokenizes the tweet text\n",
    "tokens = [\n",
    "        token.lower() \n",
    "         for tweet in textually_relevant_tweets \n",
    "             for token in tokenizer.tokenize(tweet[\"text\"])\n",
    "        ]\n",
    "tokens = list(filter(lambda x: x not in stops and len(x) > 3, tokens))\n",
    "\n",
    "print(\"Total Token Count:\", len(tokens))\n",
    "print(\"Unique Token Count:\", len(set(tokens)))\n",
    "\n",
    "tokens_freq = nltk.FreqDist(tokens)\n",
    "\n",
    "print(\"\\nFrequent Tokens:\")\n",
    "for token, count in tokens_freq.most_common(20):\n",
    "    print(token, count)\n",
    "\n",
    "# This list comprehension iterates through the tweet_list list, and for each\n",
    "#  tweet, it iterates through the hashtags list\n",
    "htags = [\n",
    "        hashtag[\"text\"].lower() \n",
    "         for tweet in textually_relevant_tweets \n",
    "             for hashtag in tweet[\"entities\"][\"hashtags\"]\n",
    "        ]\n",
    "\n",
    "print(\"\\nTotal Hashtag Count:\", len(htags))\n",
    "print(\"Unique Hashtag Count:\", len(set(htags)))\n",
    "\n",
    "htags_freq = nltk.FreqDist(htags)\n",
    "\n",
    "print(\"\\nFrequent Hashtags:\")\n",
    "for tag, count in htags_freq.most_common(20):\n",
    "    print(tag, count)\n",
    "\n",
    "# This list comprehension iterates through the tweet_list list, and for each\n",
    "#  tweet, it iterates through the author list\n",
    "authors = [tweet[\"user\"][\"screen_name\"].lower() for tweet in textually_relevant_tweets]\n",
    "\n",
    "print(\"\\nTotal Author Count:\", len(authors))\n",
    "print(\"Unique Author Count:\", len(set(authors)))\n",
    "\n",
    "author_freq = nltk.FreqDist(authors)\n",
    "\n",
    "print(\"\\nActive Users:\")\n",
    "for author, count in author_freq.most_common(20):\n",
    "    print(author, count)\n",
    "\n",
    "# This list comprehension iterates through the tweet_list list, and for each\n",
    "#  tweet, it iterates through the URL list\n",
    "urls = [\n",
    "        url[\"expanded_url\"]\n",
    "         for tweet in textually_relevant_tweets \n",
    "             for url in tweet[\"entities\"][\"urls\"]\n",
    "        ]\n",
    "\n",
    "print(\"\\nTotal URL Count:\", len(urls))\n",
    "print(\"Unique URL Count:\", len(set(urls)))\n",
    "\n",
    "urls_freq = nltk.FreqDist(urls)\n",
    "\n",
    "print(\"\\nCommon URLs:\")\n",
    "for url, count in urls_freq.most_common(20):\n",
    "    print(url, count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at these hashtags, URLs, and users, are there any new tokens we should add to our list of relevant keywords? If so, maybe add them to the crisis map we started above and re-run this notebook.\n",
    "\n",
    "## Saving Relevant Tweets\n",
    "\n",
    "For our convenience, we'll go ahead and save these tweets' JSON versions into a separate file, so we don't have to re-read them every time we do want to analysis our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(\"/Users/yutingliao/Desktop/INST728 E/relevant_tweet_output_keywords_updated.json\", \"w\") as out_file:\n",
    "    for tweet in textually_relevant_tweets:\n",
    "        out_file.write(\"%s\\n\" % json.dumps(tweet))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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